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Peter Alvaro talks about the reasons one should engage in language design and why many of us would (or should) do something so perverse as to design a language that no one will ever use. He shares some of the extreme and sometimes obnoxious opinions that guided his design process.

Featured in AI, ML & Data Engineering

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Jarul discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Jarmul is the co-founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynote speakers at QCon.ai.

How Booking.com Uses Kubernetes for Machine Learning

This eMag examines what software engineers, data engineers, and operations teams need to know about GDPR, along with the implications it has on data collection, storage and use for any organization dealing with customer data in the EU. Download Now.

Sahil Dua, a developer at Booking.com, explained how they have been able to scale machine learning (ML) models for recommending destinations and accommodation to their customers using Kubernetes, at this year's QCon London conference (PDF of slides). In particular, he stressed how the properties of a Kubernetes cluster -- elasticity and resource starvation avoidance on containers -- helps them run computationally (and data) intensive, hard to parallelize, machine learning models.

Dua provided more details on how the properties of the Kubernetes platform benefited his team and are key for Booking.com to utilise many ML models at large scale; around 1.5 million room nights are booked daily, and the site receives 400 million monthly visitors:

Kubernetes isolation - processes that run within Linux containers (and Kubernetes pods) can be isolated at the operating system level, and therefore can be orchestrated to not directly compete for resources

Elasticity - pods running ML models can auto-scale up or down based on resource consumption

Flexibility - the self-service nature of Kubernetes, and the rapid deployment of containers allows the team to quickly try out new libraries or frameworks

GPU support - Kubernetes offers support for NVIDIA GPUs (albeit this is still in alpha), it allows 20x to 50x speed improvements

Each pre-trained ML model runs as a stateless app inside a container. The container image does not include the model itself, and instead this is retrieved at startup time from Hadoop. This keeps image sizes small and avoids having to create a new image every time there is a new model, thus speeding up deployments. Once deployed, the model will be exposed via a REST API, and Kubernetes will start probing the container for readiness to receive requests for predictions, until finally traffic will start to be directed to the new container.

Besides Kubernetes' auto-scaling and load balancing, Dua revealed some other techniques used at Booking.com for optimizing latency of the models, namely keeping the model loaded in the container's memory, and warming it up after startup (by issuing an initial request to TensorFlow, Google's ML framework, where the first run is typically slower than the rest). Not all requests come from a live system; in some cases predictions can be precomputed and stored for later usage. Optimizing for throughput (amount of work done per unit of time) is more important for the latter. Batching requests and parallelizing those that are issued asynchronous helped reduce the networking overhead, and improve throughput, said Dua.

ML models need to be trained with pre-selected data sets before they are ready to provide the kind of predictions Booking.com needs. The training part of the process is also run on Kubernetes infrastructure. Base images for the containers where training takes place contain only the required frameworks (such as TensorFlow and Torch) and fetch the actual training code from a Git repository. Again this keeps container images small and avoids proliferation of new images for each new version of the code. Training data is fetched from Hadoop clusters. Once the model is ready (training workload finished), it gets exported back to Hadoop.

Additional information on the talk can be found on the QCon London website, and the video of the talk will be made available on InfoQ over the coming months.